Developed a new evaluation metrics suite for natural language processing models in the kilian-group/phantom-wiki repository, focusing on enabling nuanced model assessment and data-driven tuning. The work introduced precision, recall, and F1 scoring functions, implemented in Python, to provide a repeatable benchmarking capability and support ongoing performance tracking. By establishing a clear and extensible API, the solution allows for future metric additions and scalable evaluation pipelines. The approach emphasized clean, traceable code changes with a single, focused feature commit, reflecting a disciplined engineering process. Skills applied included data science and machine learning, with attention to reproducibility and maintainability.